Regression Analysis: Linear Predictors

This lecture covers regression analysis, focusing on linear predictors and their role in approximating outcomes. Topics include assumptions for regression modeling, interpreting coefficients, transformations of predictors and outcomes, and the use of generalized linear models. The instructor emphasizes the importance of mean-centering predictors, standardization via z-scores, and logarithmic outcomes. Beyond linear regression, the lecture introduces generalized linear models and discusses logistic regression, Poisson regression, and causal inference techniques like 'Difference in Differences'. The session concludes with a summary highlighting the advantages of using linear regression for comparing means and the need for appropriate model specification.